Advancing Fusion with Machine Learning Research Needs Workshop Report
Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle cont...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131370 |
| _version_ | 1826198109015769088 |
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| author | Humphreys, David Kupresanin, A. Boyer, M. D Canik, J. Chang, C. S Cyr, E. C Granetz, R. Hittinger, J. Kolemen, E. Lawrence, E. Pascucci, V. Patra, A. Schissel, D. |
| author2 | Massachusetts Institute of Technology. Plasma Science and Fusion Center |
| author_facet | Massachusetts Institute of Technology. Plasma Science and Fusion Center Humphreys, David Kupresanin, A. Boyer, M. D Canik, J. Chang, C. S Cyr, E. C Granetz, R. Hittinger, J. Kolemen, E. Lawrence, E. Pascucci, V. Patra, A. Schissel, D. |
| author_sort | Humphreys, David |
| collection | MIT |
| description | Abstract
Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at
https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf
). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas. |
| first_indexed | 2024-09-23T10:58:58Z |
| format | Article |
| id | mit-1721.1/131370 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:58:58Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1313702024-02-05T20:06:34Z Advancing Fusion with Machine Learning Research Needs Workshop Report Humphreys, David Kupresanin, A. Boyer, M. D Canik, J. Chang, C. S Cyr, E. C Granetz, R. Hittinger, J. Kolemen, E. Lawrence, E. Pascucci, V. Patra, A. Schissel, D. Massachusetts Institute of Technology. Plasma Science and Fusion Center Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas. 2021-09-20T17:16:46Z 2021-09-20T17:16:46Z 2020-09-26 2020-10-04T03:26:44Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131370 PUBLISHER_CC en https://doi.org/10.1007/s10894-020-00258-1 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer US Springer US |
| spellingShingle | Humphreys, David Kupresanin, A. Boyer, M. D Canik, J. Chang, C. S Cyr, E. C Granetz, R. Hittinger, J. Kolemen, E. Lawrence, E. Pascucci, V. Patra, A. Schissel, D. Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title | Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title_full | Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title_fullStr | Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title_full_unstemmed | Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title_short | Advancing Fusion with Machine Learning Research Needs Workshop Report |
| title_sort | advancing fusion with machine learning research needs workshop report |
| url | https://hdl.handle.net/1721.1/131370 |
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